Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

394
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
394

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

17‑AAG synergizes with Belinostat to exhibit a negative effect on the proliferation and invasion of MDA‑MB‑231 breast cancer cells.

Oncology reports·2020
Same author

Integrin β3 Deficiency Results in Hypertriglyceridemia via Disrupting LPL (Lipoprotein Lipase) Secretion.

Arteriosclerosis, thrombosis, and vascular biology·2020
Same author

Discovery of a Potent and Selective NF-κB-Inducing Kinase (NIK) Inhibitor That Has Anti-inflammatory Effects in Vitro and in Vivo.

Journal of medicinal chemistry·2020
Same author

Discovery of 8-Methyl-pyrrolo[1,2-<i>a</i>]pyrazin-1(2<i>H</i>)-one Derivatives as Highly Potent and Selective Bromodomain and Extra-Terminal (BET) Bromodomain Inhibitors.

Journal of medicinal chemistry·2020
Same author

Lead discovery, chemical optimization, and biological evaluation studies of novel histone methyltransferase SET7 small-molecule inhibitors.

Bioorganic & medicinal chemistry letters·2020
Same author

Compression of Cerebellar Functional Gradients in Schizophrenia.

Schizophrenia bulletin·2020

Related Experiment Video

Updated: Apr 18, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical

Li Dong1, Yangsong Zhang1, Rui Zhang1

  • 1The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Neuroimage
|January 17, 2015
PubMed
Summary
This summary is machine-generated.

A new method, eigenspace maximal information canonical correlation analysis (emiCCA), effectively identifies both linear and nonlinear relationships in neuroimaging data. This technique offers superior performance over existing methods for analyzing brain function and connectivity.

Keywords:
Eigenspace maximal information canonical correlation analysis (emiCCA)Functional magnetic resonance imaging data analysisMotor executionNonlinearityUnsupervised

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Related Experiment Videos

Last Updated: Apr 18, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Area of Science:

  • Neuroimaging analysis
  • Brain connectivity
  • Data-driven methods

Background:

  • Linear methods like canonical correlation analysis (CCA) are common for neuroimage analysis.
  • Exploring nonlinear processes in brain function requires more flexible techniques.

Purpose of the Study:

  • Introduce eigenspace maximal information canonical correlation analysis (emiCCA) for capturing linear and nonlinear relationships.
  • Evaluate emiCCA's performance against existing CCA methods.
  • Apply emiCCA to functional magnetic resonance imaging (fMRI) data.

Main Methods:

  • Developed a novel unsupervised, data-driven method: emiCCA.
  • Validated emiCCA through simulations comparing it to linear CCA and kernel CCA.
  • Implemented an emiCCA framework for fMRI data processing.

Main Results:

  • Simulations showed emiCCA outperformed linear and kernel CCA.
  • Analysis of motor execution fMRI data revealed one linear network (primary motor cortex) and several nonlinear networks (supplementary motor area, insula, cerebellum).

Conclusions:

  • emiCCA is a powerful technique for uncovering both linear and nonlinear relationships in complex datasets.
  • The identified networks suggest contributions to hand movement execution.
  • emiCCA shows promise for advanced neuroimaging data exploration.